AI-supported network techniques

Patent metadata

Field	Value
Publication number	US-12224915-B2
Application number	US-202117477667-A
Country	US
Kind code	B2
Filing date	Sep 17, 2021
Priority date	Sep 24, 2020
Publication date	Feb 11, 2025
Grant date	Feb 11, 2025

Abstract

Examples of the present disclosure relate to an AI-supported CDN. In examples, a data processing engine processes log data of a CDN node according to a model to identify an issue. An issue indication is provided to a solution generation engine, which generates a set of solutions to automatically resolve the issue. The set of solutions is provided to a solution implementation engine, which iteratively implements solutions to resolve the issue using solution implementation information associated with a given solution. Thus, the data processing engine need not have knowledge regarding the specific hardware and/or software used within the CDN. Similarly, the solution generation engine need not have knowledge of the structure of the CDN and/or configuration of devices associated with the identified issue, such that the solution implementation engine provides a layer of abstraction between a solution and the implementation-specific details used to implement the solution within the CDN.

First claim

The invention claimed is:

1. A system comprising:
at least one processor; and
memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:
receiving, from a node of a content distribution network (CDN), log data comprising one or more events associated with a computing device of the node;
processing the log data using a model to determine an issue;
selecting a solution generation engine from a plurality of solution generation engines based at least in part on the determined issue;
generating, by the selected solution generating engine and based on the determined issue, a set of solutions to resolve the issue, wherein generating the set of solutions comprises
evaluating a knowledge graph based at least in part on the determined issue to identify a cause node;
evaluating a rule of a relationship to determine whether the determined issue satisfies the rule, wherein the relationship associates a solution node with the cause node; and
based on determining the determined issue satisfies the rule, adding a solution associated with the solution node to the set of solutions;

selecting a first solution of the set of solutions; and
implementing the first solution to resolve the issue.

2. The system of claim 1 , wherein implementing the first solution to resolve the issue comprises:
identifying a set of instructions associated with the first solution; and
performing the identified set of instructions to implement the first solution.

3. The system of claim 1 , wherein the solution generation engine is selected based at least in part on the computing device.

4. The system of claim 1 , wherein the set of operations further comprises:
storing at least a part of the log data associated with the determined issue as training data; and
training an updated model using the training data.

5. The system of claim 4 , wherein the training data further comprises at least a part of the log data associated with a routine operation of the computing device.

6. The system of claim 1 , wherein the set of operations further comprises:
determining the first solution did not resolve the issue;
selecting a second solution of the set of solutions; and
implementing the second solution to resolve the issue.

7. The system of claim 1 , wherein processing the log data comprises:
identifying at least a part of the log data associated with routine operation of the computing device to generate filtered log data; and
processing the filtered log data using the model to determine the issue.

8. The system of claim 1 , wherein the model is a first model and the issue is a first issue, and wherein processing the log data to determine the issue further comprises:
processing the log data using a second model to identify a second issue; and
selecting the first issue as the determined issue based at least in part on determining a performance metric for with the first model is higher than the performance metric for the second model.

9. The system of claim 8 , wherein the performance metric is one of:
a confidence score associated with processing the log data;
an average confidence score based on historical model performance; or
a prediction accuracy.

10. The system of claim 1 , wherein the knowledge graph is generated from at least one of:
a manual;
a knowledge base article;
a trouble ticket;
current event data;
an electronic message;
a planned maintenance data base; or
a network inventory system.

11. The system of claim 1 , wherein the set of operations further comprises:
determining that a performance metric of the model is below a predetermined threshold;
based on determining that the performance metric is below the predetermined threshold, retraining the model using historical log data, wherein the historical log data comprises at least a part of the received log data; and
processing additional log data using the retrained model to determine a second issue.

12. The system of claim 1 , wherein the model is a first model and the set of operations further comprises:
determining that a performance metric of the first model is below a predetermined threshold;
based on determining that the performance metric is below the predetermined threshold, selecting a second model to use in place of the first model; and
processing additional log data using the second model to determine a second issue.

13. The system of claim 1 , wherein selecting the solution generation engine further comprises identifying a hardware device or software package associated with the issue and selecting the solution generation based on the identified hardware device or software package.

14. The system of claim 1 , wherein selecting the solution generation engine further comprises evaluating a computing functionality associated with the issue and selecting the solution generation engine based on the evaluated computing functionality.

15. A method comprising:
receiving, from a node of a content distribution network (CDN), log data comprising one or more events associated with a computing device of the node;
processing the log data using a model to determine an issue;
selecting a solution generation engine from a plurality of solution generation engines based at least in part on the determined issue;
generating, by the selected solution generation engine and based on the determined issue, a set of solutions to resolve the issue, wherein generating the set of solutions comprises evaluating a knowledge graph based at least in part on the determined issue to identify a cause node;
evaluating a rule of a relationship to determine whether the determined issue satisfies the rule, wherein the relationship associates a solution node with the cause node; and
based on determining the determined issue satisfies the rule, adding a solution associated with the solution node to the set of solutions;
selecting a first solution of the set of solutions; and
implementing the first solution to resolve the issue.

16. The method of claim 15 , wherein implementing the first solution to resolve the issue comprises:
identifying a set of instructions associated with the first solution; and
performing the identified set of instructions to implement the first solution.

17. The method of claim 15 , further comprising:
storing at least a part of the log data associated with the determined issue as training data; and
training an updated model using the training data.

18. The method of claim 17 , wherein the training data further comprises at least a part of the log data associated with a routine operation of the computing device.

19. The method of claim 15 , wherein processing the log data comprises:
identifying at least a part of the log data associated with routine operation of the computing device to generate filtered log data; and
processing the filtered log data using the model to determine the issue.

Assignees

• Level 3 Communications Llc

Inventors

• Casey Steven M
• Henning William R
• Opferman Stephen
• Borchert Todd A
• Chowdhury Rahul

Classifications

• H04L41/0631

  using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis · CPC title

• H04L43/04

  Processing captured monitoring data, e.g. for logfile generation · CPC title

• H04L41/069

  using logs of notifications; Post-processing of notifications · CPC title

• H04L41/142

  using statistical or mathematical methods · CPC title

• H04L41/0213

  Standardised network management protocols, e.g. simple network management protocol [SNMP] · CPC title